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http://dx.doi.org/10.1021/acsnano.5c09549 | DOI Listing |
[This corrects the article DOI: 10.1039/D4RA07087A.].
View Article and Find Full Text PDFJMIR Med Inform
August 2025
Division of Radiology and Biomedical Engineering, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan, 81 3-3815-5411.
Background: Recent advances in large language models have highlighted the need for high-quality multilingual medical datasets. Although Japan is a global leader in computed tomography (CT) scanner deployment and use, the absence of large-scale Japanese radiology datasets has hindered the development of specialized language models for medical imaging analysis. Despite the emergence of multilingual models and language-specific adaptations, the development of Japanese-specific medical language models has been constrained by a lack of comprehensive datasets, particularly in radiology.
View Article and Find Full Text PDFNat Med
August 2025
Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Institute for Proactive Healthcare, Shanghai Jiao Tong University, Dep
In the context of an increasing need for clinical assessments of foundation models, we developed EyeFM, a multimodal vision-language eyecare copilot, and conducted a multifaceted evaluation, including retrospective validations, multicountry efficacy validation as a clinical copilot and a double-masked randomized controlled trial (RCT). EyeFM was pretrained on 14.5 million ocular images from five imaging modalities paired with clinical texts from global, multiethnic datasets.
View Article and Find Full Text PDFGenome Biol
August 2025
Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland.
With rapid advancements in single-cell DNA sequencing (scDNA-seq), various computational methods have been developed to study evolution and call variants on single-cell level. However, modeling deletions remains challenging because they affect total coverage in ways that are difficult to distinguish from technical artifacts. We present DelSIEVE, a statistical method that infers cell phylogeny and single-nucleotide variants, accounting for deletions, from scDNA-seq data.
View Article and Find Full Text PDF